If It's Not Enough, Make It So: Reducing Authentic Data Demand in Face Recognition through Synthetic Faces

arXiv:2404.03537v1 Announce Type: new
Abstract: Recent advances in deep face recognition have spurred a growing demand for large, diverse, and manually annotated face datasets. Acquiring authentic, high-quality data for face recognition has proven to be a challenge, primarily due to privacy concerns. Large face datasets are primarily sourced from web-based images, lacking explicit user consent. In this paper, we examine whether and how synthetic face data can be used to train effective face recognition models with reduced reliance on authentic …

abstract advances arxiv authentic challenge concerns cs.cv data datasets demand diverse face face recognition privacy quality quality data recognition synthetic through type